Mobile merchants are an increasingly prevalent phenomenon. Many traditional merchants (e.g., restaurants, grocery stores, department stores, and the like) are opting to sell goods and services at locations other than their usual place of business. For example, many restaurant owners now operate food trucks in addition to their stationary dining venues. Food trucks are often more cost-effective than restaurants, and the ability to reach customers in numerous locations can provide owners with substantial financial benefit.
Mobile merchants typically “set up shop” at different locations on a day-to-day basis. Consequently, mobile merchants may want to be aware of the specific geographic locations and times of day associated with the greatest revenue potential. Further, mobile merchants may want to be aware of the specific locations associated with a high rate of fraudulent transactions so that such locations can be avoided.
Traditionally, mobile merchants have relied on personal experience to determine when and where to conduct transactions. Such information, however, may be limited to those locations at which the mobile merchant has previously set up shop. Moreover, mobile merchants may be unwilling to conduct transactions at unfamiliar locations out of concern for fraud. As such, mobile merchants may be unaware of profitable opportunities at unfamiliar geographic locations.
Moreover, existing computational solutions to the problem of determining transaction volume and fraud statistics for mobile merchants are inefficient, memory-intensive, and unreliable. For example, mobile merchants can maintain electronic records of their transactions that may be provided by the mobile merchant to a third party data analyst. To produce meaningful transaction volume and fraud statistics, however, electronic transaction records for many mobile merchants that conduct transactions at many locations must be aggregated. Consequently, such electronic transaction records may potentially be received from mobile merchants in disparate data formats. Thus, before analysis can begin, the electronic records must typically be normalized which can be a time-consuming and memory-intensive process. Moreover, since the accuracy of the resulting statistics is highly dependent on the volume of transaction data analyzed, the produced volume and fraud statistics may not accurately reflect the most desirable (or undesirable) geographic locations and times of day.
Embodiments of the invention address the above problems, and other problems, individually and collectively.